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Issue No.06 - November/December (2009 vol.29)
pp: 14-19
Kwan-Liu Ma , University of California, Davis
Scientific computing at the petascale level enables us to answer many difficult scientific questions, but the resulting data are too large to store and study directly with conventional postprocessing visualization tools. This problem will only become more severe as we reach exascale computing. A plausible, attractive solution involves processing data in situ with the simulation to reduce the data that must be transferred over networks and stored and to prepare the data for more cost-effective postprocessing visualization. The data could be reduced with compression, feature extraction, and visualization methods. This article discusses critical issues in realizing in situ visualization and data reduction and suggests important research directions.
scalability, scientific discovery, supercomputing, visualization, computer graphics
Kwan-Liu Ma, "In Situ Visualization at Extreme Scale: Challenges and Opportunities", IEEE Computer Graphics and Applications, vol.29, no. 6, pp. 14-19, November/December 2009, doi:10.1109/MCG.2009.120
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3. H. Yu, C. Wang, and K.-L. Ma, "Parallel Hierarchical Visualization of Large Time-Varying 3D Vector Fields," Proc. 2007 ACM/IEEE Conf. Supercomputing (SC 07), ACM Press, 2007, article 24.
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7. C. Wang, H. Yu, and K.-L. Ma, "Importance-Driven Time-Varying Data Visualization," IEEE Trans. Visualization and Computer Graphics, vol. 14, no. 6, 2008, pp. 1547–1554.
8. M. Glatter et al., "Visualizing Temporal Patterns in Large Multivariate Data Using Textual Pattern Matching," IEEE Trans. Visualization and Computer Graphics, vol. 14, no. 6, 2008, pp. 1467–1474.
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